Subjects -> MEDICAL SCIENCES (Total: 8695 journals)
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MEDICAL SCIENCES (2420 journals)                  1 2 3 4 5 6 7 8 | Last

Showing 1 - 200 of 3562 Journals sorted alphabetically
16 de Abril     Open Access   (Followers: 4)
3D Printing in Medicine     Open Access   (Followers: 5)
4 open     Open Access  
AADE in Practice     Hybrid Journal   (Followers: 6)
AAS Open Research     Open Access   (Followers: 2)
ABCS Health Sciences     Open Access   (Followers: 8)
Abia State University Medical Students' Association Journal     Full-text available via subscription   (Followers: 3)
AboutOpen     Open Access  
ACIMED     Open Access   (Followers: 1)
ACS Medicinal Chemistry Letters     Hybrid Journal   (Followers: 50)
Acta Bio Medica     Open Access   (Followers: 2)
Acta Bioethica     Open Access  
Acta Bioquimica Clinica Latinoamericana     Open Access   (Followers: 1)
Acta Científica Estudiantil     Open Access  
Acta Facultatis Medicae Naissensis     Open Access   (Followers: 1)
Acta Herediana     Open Access  
Acta Informatica Medica     Open Access   (Followers: 2)
Acta Medica (Hradec Králové)     Open Access  
Acta Medica Bulgarica     Open Access  
Acta Medica Colombiana     Open Access   (Followers: 1)
Acta Médica Costarricense     Open Access   (Followers: 2)
Acta Medica Indonesiana     Open Access  
Acta Medica International     Open Access  
Acta medica Lituanica     Open Access   (Followers: 1)
Acta Medica Marisiensis     Open Access   (Followers: 1)
Acta Medica Martiniana     Open Access  
Acta Medica Nagasakiensia     Open Access   (Followers: 1)
Acta Medica Peruana     Open Access   (Followers: 2)
Acta Médica Portuguesa     Open Access  
Acta Medica Saliniana     Open Access  
Acta Scientiarum. Health Sciences     Open Access   (Followers: 3)
Acupuncture & Electro-Therapeutics Research     Full-text available via subscription   (Followers: 8)
Acupuncture and Natural Medicine     Open Access  
Addiction Science & Clinical Practice     Open Access   (Followers: 9)
Addictive Behaviors Reports     Open Access   (Followers: 9)
Adıyaman Üniversitesi Sağlık Bilimleri Dergisi / Health Sciences Journal of Adıyaman University     Open Access   (Followers: 1)
Adnan Menderes Üniversitesi Sağlık Bilimleri Fakültesi Dergisi     Open Access   (Followers: 1)
Advanced Biomedical Research     Open Access  
Advanced Health Care Technologies     Open Access   (Followers: 11)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 9)
Advanced Therapeutics     Hybrid Journal   (Followers: 1)
Advances in Bioscience and Clinical Medicine     Open Access   (Followers: 7)
Advances in Cell and Gene Therapy     Hybrid Journal   (Followers: 1)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 27)
Advances in Clinical Radiology     Full-text available via subscription   (Followers: 4)
Advances in Life Course Research     Hybrid Journal   (Followers: 11)
Advances in Lipobiology     Full-text available via subscription   (Followers: 2)
Advances in Medical Education and Practice     Open Access   (Followers: 32)
Advances in Medical Ethics     Open Access   (Followers: 6)
Advances in Medical Research     Open Access   (Followers: 3)
Advances in Medical Sciences     Hybrid Journal   (Followers: 11)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 6)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 5)
Advances in Molecular Oncology     Open Access   (Followers: 2)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7)
Advances in Parkinson's Disease     Open Access   (Followers: 2)
Advances in Phytomedicine     Full-text available via subscription   (Followers: 2)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 21)
Advances in Regenerative Medicine     Open Access   (Followers: 4)
Advances in Skeletal Muscle Function Assessment     Open Access  
Advances in Therapy     Hybrid Journal   (Followers: 5)
Advances in Traditional Medicine     Hybrid Journal   (Followers: 8)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 15)
Advances in Virus Research     Full-text available via subscription   (Followers: 6)
Advances in Wound Care     Hybrid Journal   (Followers: 14)
Aerospace Medicine and Human Performance     Full-text available via subscription   (Followers: 13)
African Health Sciences     Open Access   (Followers: 5)
African Journal of Biomedical Research     Open Access   (Followers: 1)
African Journal of Clinical and Experimental Microbiology     Open Access   (Followers: 4)
African Journal of Laboratory Medicine     Open Access   (Followers: 2)
African Journal of Medical and Health Sciences     Open Access   (Followers: 4)
African Journal of Thoracic and Critical Care Medicine     Open Access  
African Journal of Trauma     Open Access   (Followers: 1)
Afrimedic Journal     Open Access   (Followers: 3)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
AIDS Research and Human Retroviruses     Hybrid Journal   (Followers: 9)
AJOB Empirical Bioethics     Hybrid Journal   (Followers: 3)
AJSP: Reviews & Reports     Hybrid Journal   (Followers: 1)
Aktuelle Ernährungsmedizin     Hybrid Journal   (Followers: 6)
Al-Azhar Assiut Medical Journal     Open Access   (Followers: 2)
Al-Qadisiah Medical Journal     Open Access   (Followers: 1)
Alerta : Revista Científica del Instituto Nacional de Salud     Open Access  
Alexandria Journal of Medicine     Open Access   (Followers: 1)
Allgemeine Homöopathische Zeitung     Hybrid Journal   (Followers: 3)
Alpha Omegan     Full-text available via subscription  
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 2)
Althea Medical Journal     Open Access   (Followers: 2)
American Journal of Biomedical Engineering     Open Access   (Followers: 15)
American Journal of Biomedical Research     Open Access   (Followers: 2)
American Journal of Biomedicine     Full-text available via subscription   (Followers: 7)
American Journal of Chinese Medicine, The     Hybrid Journal   (Followers: 4)
American Journal of Clinical Medicine Research     Open Access   (Followers: 8)
American Journal of Family Therapy     Hybrid Journal   (Followers: 10)
American Journal of Law & Medicine     Full-text available via subscription   (Followers: 12)
American Journal of Lifestyle Medicine     Hybrid Journal   (Followers: 7)
American Journal of Managed Care     Full-text available via subscription   (Followers: 13)
American Journal of Medical Case Reports     Open Access   (Followers: 3)
American Journal of Medical Sciences and Medicine     Open Access   (Followers: 6)
American Journal of Medicine     Hybrid Journal   (Followers: 50)
American Journal of Medicine and Medical Sciences     Open Access   (Followers: 2)
American Journal of Medicine Studies     Open Access   (Followers: 3)
American Journal of Medicine Supplements     Full-text available via subscription   (Followers: 3)
American Journal of the Medical Sciences     Hybrid Journal   (Followers: 12)
American Journal on Addictions     Hybrid Journal   (Followers: 11)
American medical news     Free   (Followers: 3)
American Medical Writers Association Journal     Full-text available via subscription   (Followers: 6)
Amyloid: The Journal of Protein Folding Disorders     Hybrid Journal   (Followers: 5)
Anales de la Facultad de Medicina     Open Access  
Anales de la Facultad de Medicina, Universidad de la República, Uruguay     Open Access  
Anales del Sistema Sanitario de Navarra     Open Access   (Followers: 1)
Analgesia & Resuscitation : Current Research     Hybrid Journal   (Followers: 7)
Anatolian Clinic the Journal of Medical Sciences     Open Access  
Anatomica Medical Journal     Open Access  
Anatomical Science International     Hybrid Journal   (Followers: 3)
Anatomical Sciences Education     Hybrid Journal   (Followers: 2)
Anatomy     Open Access   (Followers: 3)
Anatomy Research International     Open Access   (Followers: 4)
Androgens : Clinical Research and Therapeutics     Open Access   (Followers: 2)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3)
Ankara Medical Journal     Open Access   (Followers: 2)
Ankara Üniversitesi Tıp Fakültesi Mecmuası     Open Access  
Annales de Pathologie     Full-text available via subscription  
Annales des Sciences de la Santé     Open Access  
Annales françaises d'Oto-rhino-laryngologie et de Pathologie Cervico-faciale     Full-text available via subscription   (Followers: 3)
Annals of African Medicine     Open Access   (Followers: 2)
Annals of Anatomy - Anatomischer Anzeiger     Hybrid Journal   (Followers: 3)
Annals of Bioanthropology     Open Access   (Followers: 5)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 20)
Annals of Biomedical Sciences     Full-text available via subscription   (Followers: 4)
Annals of Clinical Hypertension     Open Access  
Annals of Clinical Microbiology and Antimicrobials     Open Access   (Followers: 15)
Annals of Family Medicine     Open Access   (Followers: 18)
Annals of Health Research     Open Access   (Followers: 2)
Annals of Ibadan Postgraduate Medicine     Open Access  
Annals of Medical and Health Sciences Research     Open Access   (Followers: 8)
Annals of Medicine     Hybrid Journal   (Followers: 12)
Annals of Medicine and Surgery     Open Access   (Followers: 7)
Annals of Medicine and Surgery Case Reports     Open Access   (Followers: 1)
Annals of Medicine and Surgery Protocols     Open Access   (Followers: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 13)
Annals of Musculoskeletal Medicine     Open Access   (Followers: 2)
Annals of Nigerian Medicine     Open Access   (Followers: 1)
Annals of Rehabilitation Medicine     Open Access   (Followers: 1)
Annals of Saudi Medicine     Open Access  
Annals of the College of Medicine, Mosul     Open Access   (Followers: 1)
Annals of the New York Academy of Sciences     Hybrid Journal   (Followers: 5)
Annals of The Royal College of Surgeons of England     Full-text available via subscription   (Followers: 3)
Annals of the RussianAacademy of Medical Sciences     Open Access  
Annual Reports in Medicinal Chemistry     Full-text available via subscription   (Followers: 7)
Annual Reports on NMR Spectroscopy     Full-text available via subscription   (Followers: 5)
Annual Review of Medicine     Full-text available via subscription   (Followers: 18)
Anthropological Review     Open Access   (Followers: 27)
Anthropologie et santé     Open Access   (Followers: 5)
Antibiotics     Open Access   (Followers: 9)
Antibodies     Open Access   (Followers: 2)
Antibody Reports     Open Access   (Followers: 1)
Antibody Technology Journal     Open Access   (Followers: 1)
Antibody Therapeutics     Open Access   (Followers: 1)
Anuradhapura Medical Journal     Open Access  
Anwer Khan Modern Medical College Journal     Open Access   (Followers: 2)
Apmis     Hybrid Journal   (Followers: 2)
Apparence(s)     Open Access   (Followers: 1)
Applied Clinical Informatics     Hybrid Journal   (Followers: 5)
Applied Clinical Research, Clinical Trials and Regulatory Affairs     Hybrid Journal   (Followers: 2)
Applied Medical Informatics     Open Access   (Followers: 14)
Arab Journal of Nephrology and Transplantation     Open Access   (Followers: 1)
Arabian Journal of Scientific Research / المجلة العربية للبحث العلمي     Open Access   (Followers: 1)
Archive of Biomedical Science and Engineering     Open Access   (Followers: 1)
Archive of Clinical Medicine     Open Access   (Followers: 1)
Archive of Community Health     Open Access   (Followers: 1)
Archives Medical Review Journal / Arşiv Kaynak Tarama Dergisi     Open Access  
Archives of Asthma, Allergy and Immunology     Open Access  
Archives of Clinical Hypertension     Open Access   (Followers: 2)
Archives of Medical and Biomedical Research     Open Access   (Followers: 3)
Archives of Medical Laboratory Sciences     Open Access   (Followers: 1)
Archives of Medicine and Health Sciences     Open Access   (Followers: 5)
Archives of Medicine and Surgery     Open Access   (Followers: 1)
Archives of Organ Transplantation     Open Access   (Followers: 2)
Archives of Preventive Medicine     Open Access   (Followers: 3)
Archives of Pulmonology and Respiratory Care     Open Access   (Followers: 2)
Archives of Renal Diseases and Management     Open Access   (Followers: 2)
Archives of Trauma Research     Open Access   (Followers: 4)
Archivos de Medicina (Manizales)     Open Access   (Followers: 1)
ArgoSpine News & Journal     Hybrid Journal  
Arquivos Brasileiros de Oftalmologia     Open Access   (Followers: 1)
Arquivos de Ciências da Saúde     Open Access  
Arquivos de Medicina     Open Access   (Followers: 1)
Ars Medica : Revista de Ciencias Médicas     Open Access  
ARS Medica Tomitana     Open Access   (Followers: 1)
Art Therapy: Journal of the American Art Therapy Association     Hybrid Journal   (Followers: 19)
Arterial Hypertension     Open Access   (Followers: 1)
Artificial Intelligence in Medicine     Hybrid Journal   (Followers: 21)
Artificial Organs     Hybrid Journal   (Followers: 1)
ASHA Leader     Open Access   (Followers: 6)
Asia Pacific Family Medicine Journal     Open Access   (Followers: 4)
Asia Pacific Journal of Clinical Nutrition     Full-text available via subscription   (Followers: 13)
Asia Pacific Journal of Clinical Trials : Nervous System Diseases     Open Access   (Followers: 1)

        1 2 3 4 5 6 7 8 | Last

Similar Journals
Journal Cover
Applied Clinical Informatics
Journal Prestige (SJR): 0.624
Citation Impact (citeScore): 1
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1869-0327
Published by Thieme Publishing Group Homepage  [242 journals]
  • Application of Human Factors Methods to Understand Missed Follow-up of
           Abnormal Test Results
    • Authors: Rogith; Deevakar, Satterly, Tyler, Singh, Hardeep, Sittig, Dean F., Russo, Elise, Smith, Michael W., Roosan, Don, Bhise, Viraj, Murphy, Daniel R.
      Pages: 692 - 698
      Abstract: Objective This study demonstrates application of human factors methods for understanding causes for lack of timely follow-up of abnormal test results (“missed results”) in outpatient settings. Methods We identified 30 cases of missed test results by querying electronic health record data, developed a critical decision method (CDM)-based interview guide to understand decision-making processes, and interviewed physicians who ordered these tests. We analyzed transcribed responses using a contextual inquiry (CI)-based methodology to identify contextual factors contributing to missed results. We then developed a CI-based flow model and conducted a fault tree analysis (FTA) to identify hierarchical relationships between factors that delayed action. Results The flow model highlighted barriers in information flow and decision making, and the hierarchical model identified relationships between contributing factors for delayed action. Key findings including underdeveloped methods to track follow-up, as well as mismatches, in communication channels, timeframes, and expectations between patients and physicians. Conclusion This case report illustrates how human factors–based approaches can enable analysis of contributing factors that lead to missed results, thus informing development of preventive strategies to address them.
      Citation: Appl Clin Inform 2020; 11: 692-698
      PubDate: 2020-10-21T00:00:00+01:00
      DOI: 10.1055/s-0040-1716537
      Issue No: Vol. 11, No. 05 (2020)
       
  • User-Centered Clinical Display Design Issues for Inpatient Providers
    • Authors: Lasko; Thomas A., Owens, David A., Fabbri, Daniel, Wanderer, Jonathan P., Genkins, Julian Z., Novak, Laurie L.
      Pages: 700 - 709
      Abstract: Background Suboptimal information display in electronic health records (EHRs) is a notorious pain point for users. Designing an effective display is difficult, due in part to the complex and varied nature of clinical practice. Objective This article aims to understand the goals, constraints, frustrations, and mental models of inpatient medical providers when accessing EHR data, to better inform the display of clinical information. Methods A multidisciplinary ethnographic study of inpatient medical providers. Results Our participants' primary goal was usually to assemble a clinical picture around a given question, under the constraints of time pressure and incomplete information. To do so, they tend to use a mental model of multiple layers of abstraction when thinking of patients and disease; they prefer immediate pattern recognition strategies for answering clinical questions, with breadth-first or depth-first search strategies used subsequently if needed; and they are sensitive to data relevance, completeness, and reliability when reading a record. Conclusion These results conflict with the ubiquitous display design practice of separating data by type (test results, medications, notes, etc.), a mismatch that is known to encumber efficient mental processing by increasing both navigation burden and memory demands on users. A popular and obvious solution is to select or filter the data to display exactly what is presumed to be relevant to the clinical question, but this solution is both brittle and mistrusted by users. A less brittle approach that is more aligned with our users' mental model could use abstraction to summarize details instead of filtering to hide data. An abstraction-based approach could allow clinicians to more easily assemble a clinical picture, to use immediate pattern recognition strategies, and to adjust the level of displayed detail to their particular needs. It could also help the user notice unanticipated patterns and to fluidly shift attention as understanding evolves.
      Citation: Appl Clin Inform 2020; 11: 700-709
      PubDate: 2020-10-21T00:00:00+01:00
      DOI: 10.1055/s-0040-1716746
      Issue No: Vol. 11, No. 05 (2020)
       
  • Policy Statement on Clinical Informatics Fellowships and the Future of
           Informatics-Driven Medicine
    • Authors: Kannry; Joseph, Smith, Jeff, Mohan, Vishnu, Levy, Bruce, Finnell, John, Lehmann, Christoph U.
      Pages: 710 - 713
      Abstract: Board certified clinical informaticians provide expertise in leveraging health IT (HIT) and health data for patient care and quality improvement. Clinical Informatics experts possess the requisite skills and competencies to make systems-level improvements in care delivery using HIT, workflow and data analytics, knowledge acquisition, clinical decision support, data visualization, and related informatics tools. However, these physicians lack structured and sustained funding because they have no billing codes. The sustainability and growth of this new and promising medical subspecialty is threatened by outdated and inconsistent funding models that fail to support the education and professional growth of clinical informaticians. The Clinical Informatics Program Directors' Community is calling upon the Centers for Medicare and Medicaid Services to consider novel funding structures and programs through its Innovation Center for Clinical Informatics Fellowship training. Only through structural and sustained funding for Clinical Informatics fellows will be able to fully develop the potential of electronic health records to improve the quality, safety, and cost of clinical care.
      Citation: Appl Clin Inform 2020; 11: 710-713
      PubDate: 2020-10-28T00:00:00+0100
      DOI: 10.1055/s-0040-1717117
      Issue No: Vol. 11, No. 05 (2020)
       
  • Development of a Taxonomy for Medication-Related Patient Safety Events
           Related to Health Information Technology in Pediatrics
    • Authors: Wyatt; Kirk D., Benning, Tyler J., Morgenthaler, Timothy I., Arteaga, Grace M.
      Pages: 714 - 724
      Abstract: Background Although electronic health records (EHRs) are designed to improve patient safety, they have been associated with serious patient harm. An agreed-upon and standard taxonomy for classifying health information technology (HIT) related patient safety events does not exist. Objectives We aimed to develop and evaluate a taxonomy for medication-related patient safety events associated with HIT and validate it using a set of events involving pediatric patients. Methods We performed a literature search to identify existing classifications for HIT-related safety events, which were assessed using real-world pediatric medication-related patient safety events extracted from two sources: patient safety event reporting system (ERS) reports and information technology help desk (HD) tickets. A team of clinical and patient safety experts used iterative tests of change and consensus building to converge on a single taxonomy. The final devised taxonomy was applied to pediatric medication-related events assess its characteristics, including interrater reliability and agreement. Results Literature review identified four existing classifications for HIT-related patient safety events, and one was iteratively adapted to converge on a singular taxonomy. Safety events relating to usability accounted for a greater proportion of ERS reports, compared with HD tickets (37 vs. 20%, p = 0.022). Conversely, events pertaining to incorrect configuration accounted for a greater proportion of HD tickets, compared with ERS reports (63 vs. 8%, p 
      Citation: Appl Clin Inform 2020; 11: 714-724
      PubDate: 2020-10-28T00:00:00+0100
      DOI: 10.1055/s-0040-1717084
      Issue No: Vol. 11, No. 05 (2020)
       
  • Accuracy of an Electronic Health Record Patient Linkage Module Evaluated
           between Neighboring Academic Health Care Centers
    • Authors: Ross; Mindy K., Sanz, Javier, Tep, Brian, Follett, Rob, Soohoo, Spencer L., Bell, Douglas S.
      Pages: 725 - 732
      Abstract: Background Patients often seek medical treatment among different health care organizations, which can lead to redundant tests and treatments. One electronic health record (EHR) platform, Epic Systems, uses a patient linkage tool called Care Everywhere (CE), to match patients across institutions. To the extent that such linkages accurately identify shared patients across organizations, they would hold potential for improving care. Objective This study aimed to understand how accurate the CE tool with default settings is to identify identical patients between two neighboring academic health care systems in Southern California, The University of California Los Angeles (UCLA) and Cedars-Sinai Medical Center. Methods We studied CE patient linkage queries received at UCLA from Cedars-Sinai between November 1, 2016, and April 30, 2017. We constructed datasets comprised of linkages (“successful” queries), as well as nonlinkages (“unsuccessful” queries) during this time period. To identify false positive linkages, we screened the “successful” linkages for potential errors and then manually reviewed all that screened positive. To identify false-negative linkages, we applied our own patient matching algorithm to the “unsuccessful” queries and then manually reviewed a sample to identify missed patient linkages. Results During the 6-month study period, Cedars-Sinai attempted to link 181,567 unique patient identities to records at UCLA. CE made 22,923 “successful” linkages and returned 158,644 “unsuccessful” queries among these patients. Manual review of the screened “successful” linkages between the two institutions determined there were no false positives. Manual review of a sample of the “unsuccessful” queries (n = 623), demonstrated an extrapolated false-negative rate of 2.97% (95% confidence interval [CI]: 1.6–4.4%). Conclusion We found that CE provided very reliable patient matching across institutions. The system missed a few linkages, but the false-negative rate was low and there were no false-positive matches over 6 months of use between two nearby institutions.
      Citation: Appl Clin Inform 2020; 11: 725-732
      PubDate: 2020-11-04T00:00:00+0100
      DOI: 10.1055/s-0040-1718374
      Issue No: Vol. 11, No. 05 (2020)
       
  • Inpatient Telehealth Tools to Enhance Communication and Decrease Personal
           Protective Equipment Consumption during Disaster Situations: A Case Study
           during the COVID-19 Pandemic
    • Authors: Ong; Shawn Y., Stump, Lisa, Zawalich, Matthew, Edwards, Lisa, Stanton, Glynn, Matthews, Michael, Hsiao, Allen L.
      Pages: 733 - 741
      Abstract: Background As the coronavirus disease 2019 pandemic exerts unprecedented stress on hospitals, health care systems have quickly deployed innovative technology solutions to decrease personal protective equipment (PPE) use and augment patient care capabilities. Telehealth technology use is established in the ambulatory setting, but not yet widely deployed at scale for inpatient care. Objectives This article presents and describes our experience with evaluating and implementing inpatient telehealth technologies in a large health care system with the goals of reducing use of PPE while enhancing communication for health care workers and patients. Methods We discovered use cases for inpatient telehealth revealed as a result of an immense patient surge requiring large volumes of PPE. In response, we assessed various consumer products to address the use cases for our health system. Results We identified 13 use cases and eight device options. During device setup and implementation, challenges and solutions were identified in five areas: security/privacy, device availability and setup, device functionality, physical setup, and workflow and device usage. This enabled deployment of more than 1,800 devices for inpatient telehealth across seven hospitals with positive feedback from health care staff. Conclusion Large-scale setup and distribution of consumer devices is feasible for inpatient telehealth use cases. Our experience highlights operational barriers and potential solutions for health systems looking to preserve PPE and enhance vital communication.
      Citation: Appl Clin Inform 2020; 11: 733-741
      PubDate: 2020-11-04T00:00:00+0100
      DOI: 10.1055/s-0040-1719180
      Issue No: Vol. 11, No. 05 (2020)
       
  • Transitions from One Electronic Health Record to Another: Challenges,
           Pitfalls, and Recommendations
    • Authors: Huang; Chunya, Koppel, Ross, McGreevey, John D., Craven, Catherine K., Schreiber, Richard
      Pages: 742 - 754
      Abstract: Objective We address the challenges of transitioning from one electronic health record (EHR) to another—a near ubiquitous phenomenon in health care. We offer mitigating strategies to reduce unintended consequences, maximize patient safety, and enhance health care delivery. Methods We searched PubMed and other sources to identify articles describing EHR-to-EHR transitions. We combined these references with the authors' extensive experience to construct a conceptual schema and to offer recommendations to facilitate transitions. Results Our PubMed query retrieved 1,351 citations: 43 were relevant for full paper review and 18 met the inclusion criterion of focus on EHR-to-EHR transitions. An additional PubMed search yielded 1,014 citations, for which we reviewed 74 full papers and included 5. We supplemented with additional citations for a total of 70 cited. We distinguished 10 domains in the literature that overlap yet present unique and salient opportunities for successful transitions and for problem mitigation. Discussion There is scant literature concerning EHR-to-EHR transitions. Identified challenges include financial burdens, personnel resources, patient safety threats from limited access to legacy records, data integrity during migration, cybersecurity, and semantic interoperability. Transition teams must overcome inadequate human infrastructure, technical challenges, security gaps, unrealistic providers' expectations, workflow changes, and insufficient training and support—all factors affecting potential clinician burnout. Conclusion EHR transitions are remarkably expensive, laborious, personnel devouring, and time consuming. The paucity of references in comparison to the topic's salience reinforces the necessity for this type of review and analysis. Prudent planning may streamline EHR transitions and reduce expenses. Mitigating strategies, such as preservation of legacy data, managing expectations, and hiring short-term specialty consultants can overcome some of the greatest hurdles. A new medical subject headings (MeSH) term for EHR transitions would facilitate further research on this topic.
      Citation: Appl Clin Inform 2020; 11: 742-754
      PubDate: 2020-11-11T00:00:00+0100
      DOI: 10.1055/s-0040-1718535
      Issue No: Vol. 11, No. 05 (2020)
       
  • Ethical Considerations on Pediatric Genetic Testing Results in Electronic
           Health Records
    • Authors: Kanungo; Shibani, Barr, Jayne, Crutchfield, Parker, Fealko, Casey, Soares, Neelkamal
      Pages: 755 - 763
      Abstract: Background Advances in technology and access to expanded genetic testing have resulted in more children and adolescents receiving genetic testing for diagnostic and prognostic purposes. With increased adoption of the electronic health record (EHR), genetic testing is increasingly resulted in the EHR. However, this leads to challenges in both storage and disclosure of genetic results, particularly when parental results are combined with child genetic results. Privacy and Ethical Considerations Accidental disclosure and erroneous documentation of genetic results can occur due to the nature of their presentation in the EHR and documentation processes by clinicians. Genetic information is both sensitive and identifying, and requires a considered approach to both timing and extent of disclosure to families and access to clinicians. Methods This article uses an interdisciplinary approach to explore ethical issues surrounding privacy, confidentiality of genetic data, and access to genetic results by health care providers and family members, and provides suggestions in a stakeholder format for best practices on this topic for clinicians and informaticians. Suggestions are made for clinicians on documenting and accessing genetic information in the EHR, and on collaborating with genetics specialists and disclosure of genetic results to families. Additional considerations for families including ethics around results of adolescents and special scenarios for blended families and foster minors are also provided. Finally, administrators and informaticians are provided best practices on both institutional processes and EHR architecture, including security and access control, with emphasis on the minimum necessary paradigm and parent/patient engagement and control of the use and disclosure of data. Conclusion The authors hope that these best practices energize specialty societies to craft practice guidelines on genetic information management in the EHR with interdisciplinary input that addresses all stakeholder needs.
      Citation: Appl Clin Inform 2020; 11: 755-763
      PubDate: 2020-11-11T00:00:00+0100
      DOI: 10.1055/s-0040-1718753
      Issue No: Vol. 11, No. 05 (2020)
       
  • Patient Portal, Patient-Generated Images, and Medical Decision-Making in a
           Pediatric Ambulatory Setting
    • Authors: Ginting; Karolin, Stolfi, Adrienne, Wright, Jordan, Omoloja, Abiodun
      Pages: 764 - 768
      Abstract: Background Electronic health record (EHR) patient portals are a secure electronic method of communicating with health care providers. In addition to sending secure messages, images, and videos generated by families can be sent to providers securely. With the widespread use of smart phones, there has been an increase in patient-generated images (PGI) sent to providers via patient portals. There are few studies that have evaluated the role of PGI in medical decision-making. Objectives The study aimed to characterize PGI sent to providers via a patient portal, determine how often PGI-affected medical decision-making, and determine the rate of social PGI sent via patient portal. Methods A retrospective chart review of PGI uploaded to a children's hospital's ambulatory patient portal from January 2011 to December 2017 was conducted. Data collected included patient demographics, number and type of images sent, person sending images (patient or parent/guardian), and whether an image-affected medical decision-making. Images were classified as medical related (e.g., blood glucose readings and skin rashes), nonmedical or administrative related (e.g., medical clearance or insurance forms), and social (e.g., self-portraits and camp pictures). Results One hundred forty-three individuals used the portal a total of 358 times, sending 507 images over the study period. Mean (standard deviation) patient age was 9.5 (5.9) years, 50% were females, 89% were White, and 64% had private insurance. About 9% of images were sent directly by patients and the rest by parents/guardians. A total of 387 (76%) images were sent for medical related reasons, 20% for nonmedical, and 4% were deemed social images. Of the 387 medical related images, 314 (81%) affected medical decision-making. Conclusion PGI-affected medical decision-making in most cases. Additional studies are needed to characterize use of PGI in the pediatric population.
      Citation: Appl Clin Inform 2020; 11: 764-768
      PubDate: 2020-11-18T00:00:00+0100
      DOI: 10.1055/s-0040-1718754
      Issue No: Vol. 11, No. 05 (2020)
       
  • Augmenting the Clinical Data Sources for Enigmatic Diseases: A
           Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in
           Endometriosis
    • Authors: Ensari; Ipek, Pichon, Adrienne, Lipsky-Gorman, Sharon, Bakken, Suzanne, Elhadad, Noémie
      Pages: 769 - 784
      Abstract: Background Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement. Objectives This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR. Methods This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking. Results Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%). Conclusion For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.
      Citation: Appl Clin Inform 2020; 11: 769-784
      PubDate: 2020-11-18T00:00:00+0100
      DOI: 10.1055/s-0040-1718755
      Issue No: Vol. 11, No. 05 (2020)
       
  • Visualizing Opportunity Index Data Using a Dashboard Application: A Tool
           to Communicate Infant Mortality-Based Area Deprivation Index Information
    • Authors: Fareed; Naleef, Swoboda, Christine M., Jonnalagadda, Pallavi, Griesenbrock, Tyler, Gureddygari, Harish R., Aldrich, Alison
      Pages: 515 - 527
      Abstract: Background An area deprivation index (ADI) is a geographical measure that accounts for socioeconomic factors (e.g., crime, health, and education). The state of Ohio developed an ADI associated with infant mortality: Ohio Opportunity Index (OOI). However, a powerful tool to present this information effectively to stakeholders was needed. Objectives We present a real use-case by documenting the design, development, deployment, and training processes associated with a dashboard solution visualizing ADI data. Methods The Opportunity Index Dashboard (OID) allows for interactive exploration of the OOI and its seven domains—transportation, education, employment, housing, health, access to services, and crime. We used a user-centered design approach involving feedback sessions with stakeholders, who included representatives from project sponsors and subject matter experts. We assessed the usability of the OID based on the effectiveness, efficiency, and satisfaction dimensions. The process of designing, developing, deploying, and training users in regard to the OID is described. Results We report feedback provided by stakeholders for the OID categorized by function, content, and aesthetics. The OID has multiple, interactive components: choropleth map displaying OOI scores for a specific census tract, graphs presenting OOI or domain scores between tracts to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. Changes based on parameter and filter selections are described using a general use-case. In the usability evaluation, the median task completion success rate was 83% and the median system usability score was 68. Conclusion The OID could assist health care leaders in making decisions that enhance care delivery and policy decision making regarding infant mortality. The dashboard helps communicate deprivation data across domains in a clear and concise manner. Our experience building this dashboard presents a template for developing dashboards that can address other health priorities.
      Citation: Appl Clin Inform 2020; 11: 515-527
      PubDate: 2020-08-05T00:00:00+01:00
      DOI: 10.1055/s-0040-1714249
      Issue No: Vol. 11, No. 04 (2020)
       
  • Usability Testing a Potentially Inappropriate Medication Dashboard: A Core
           Component of the Dashboard Development Process
    • Authors: Richter Lagha; Regina, Burningham, Zachary, Sauer, Brian C., Leng, Jianwei, Peters, Celena, Huynh, Tina, Patel, Shardool, Halwani, Ahmad S., Kramer, B. Josea
      Pages: 528 - 534
      Abstract: Background With the increased usage of dashboard reporting systems to monitor and track patient panels by clinical users, developers must ensure that the information displays they produce are accurate and intuitive. When evaluating usability of a clinical dashboard among potential end users, developers oftentimes rely on methods such as questionnaires as opposed to other, more time-intensive strategies that incorporate direct observation. Objectives Prior to release of the potentially inappropriate medication (PIM) clinical dashboard, designed to facilitate completion of a quality improvement project by clinician scholars enrolled in the Veterans Affairs (VA) workforce development Geriatric Scholars Program (GSP), we evaluated the usability of the system. This article describes the process of usability testing a dashboard reporting system with clinicians using direct observation and think-aloud moderating techniques. Methods We developed a structured interview protocol that combines virtual observation, think-aloud moderating techniques, and retrospective questioning of the overall user experience, including use of the System Usability Scale (SUS). Thematic analysis was used to analyze field notes from the interviews of three GSP alumni. Results Our structured approach to usability testing identified specific functional problems with the dashboard reporting system that were missed by results from the SUS. Usability testing lead to overall improvements in the intuitive use of the system, increased data transparency, and clarification of the dashboard's purpose. Conclusion Reliance solely on questionnaires and surveys at the end stages of dashboard development can mask potential functional problems that will impede proper usage and lead to misinterpretation of results. A structured approach to usability testing in the developmental phase is an important tool for developers of clinician friendly systems for displaying easily digested information and tracking outcomes for the purpose of quality improvement.
      Citation: Appl Clin Inform 2020; 11: 528-534
      PubDate: 2020-08-12T00:00:00+01:00
      DOI: 10.1055/s-0040-1714693
      Issue No: Vol. 11, No. 04 (2020)
       
  • Development of a Web-Based Nonoperative Small Bowel Obstruction Treatment
           Pathway App
    • Authors: Lyu; Heather, Manca, Caitlin, McGrath, Casey, Beloff, Jennifer, Plaks, Nina, Postilnik, Anatoly, Borchers, Amanda, Diaz, Nicasio, McGovern, Sean, Havens, Joaquim, Kachalia, Allen, Landman, Adam
      Pages: 535 - 543
      Abstract: Objective An electronic pathway for the management of adhesive small bowel obstruction (SBO) was built and implemented on top of the electronic health record. The aims of this study are to describe the development of the electronic pathway and to report early outcomes. Methods The electronic SBO pathway was designed and implemented at a single institution. All patients admitted to a surgical service with a diagnosis of adhesive SBO were enrolled. Outcomes were compared across three time periods: (1) patients not placed on either pathway from September 2013 through December 2014, (2) patients enrolled in the paper pathway from January 2017 through January 2018, and (3) patients enrolled in the electronic pathway from March through October 2018. The electronic SBO pathway pulls real-time data from the electronic health record to prepopulate the evidence-based algorithm. Outcomes measured included length of stay (LOS), time to surgery, readmission, surgery, and need for bowel resection. Comparative analyses were completed with Pearson's chi-squared, analysis of variance, and Kruskal–Wallis tests. Results There were 46 patients enrolled in the electronic pathway compared with 93 patients on the paper pathway, and 101 nonpathway patients. Median LOS was lower in both pathway cohorts compared with those not on either pathway (3 days [range 1–11] vs. 3 days [range 1–27] vs. 4 days [range 1–13], p = 0.04). Rates of readmission, surgery, time to surgery, and bowel resection were not significantly different across the three groups. Conclusion It is feasible to implement and utilize an electronic, evidence-based clinical pathway for adhesive SBOs. Use of the electronic and paper pathways was associated with decreased hospital LOS for patients with adhesive SBOs.
      Citation: Appl Clin Inform 2020; 11: 535-543
      PubDate: 2020-08-19T00:00:00+01:00
      DOI: 10.1055/s-0040-1715478
      Issue No: Vol. 11, No. 04 (2020)
       
  • Influence of Connected Health Interventions for Adherence to
           Cardiovascular Disease Prevention: A Scoping Review
    • Authors: Agher; Dahbia, Sedki, Karima, Tsopra, Rosy, Despres, Sylvie, Jaulent, Marie-Christine
      Pages: 544 - 555
      Abstract: Background Recent health care developments include connected health interventions to improve chronic disease management and/or promote actions reducing aggravating risk factors for conditions such as cardiovascular diseases. Adherence is one of the main challenges for ensuring the correct use of connected health interventions over time. Objective This scoping review deals with the connected health interventions used in interventional studies, describing the ways in which these interventions and their functions effectively help patients to deal with cardiovascular risk factors over time, in their own environments. The objective is to acquire knowledge and highlight current trends in this field, which is currently both productive and immature. Methods A structured literature review was constructed from Medline-indexed journals in PubMed. We established inclusion criteria relating to three dimensions (cardiovascular risk factors, connected health interventions, and level of adherence). Our initial search yielded 98 articles; 78 were retained after screening on the basis of title and abstract, 49 articles underwent full-text screening, and 24 were finally retained for the analysis, according to preestablished inclusion criteria. We excluded studies of invasive interventions and studies not dealing with digital health. We extracted a description of the connected health interventions from data for the population or end users. Results We performed a synthetic analysis of outcomes, based on the distribution of bibliometrics, and identified several connected health interventions and main characteristics affecting adherence. Our analysis focused on three types of user action: to read, to do, and to connect. Finally, we extracted current trends in characteristics: connect, adherence, and influence. Conclusion Connected health interventions for prevention are unlikely to affect outcomes significantly unless other characteristics and user preferences are considered. Future studies should aim to determine which connected health design combinations are the most effective for supporting long-term changes in behavior and for preventing cardiovascular disease risks.
      Citation: Appl Clin Inform 2020; 11: 544-555
      PubDate: 2020-08-19T00:00:00+01:00
      DOI: 10.1055/s-0040-1715649
      Issue No: Vol. 11, No. 04 (2020)
       
  • Toward Understanding the Value of Missing Social Determinants of Health
           Data in Care Transition Planning
    • Authors: Feldman; Sue S., Davlyatov, Ganisher, Hall, Allyson G.
      Pages: 556 - 563
      Abstract: Background Social determinants of health play an important role in the likelihood of readmission and therefore should be considered in care transition planning. Unfortunately, some social determinants that can be of value to care transition planners are missing in the electronic health record. Rather than trying to understand the value of data that are missing, decision makers often exclude these data. This exclusion can lead to failure to design appropriate care transition programs, leading to readmissions. Objectives This article examines the value of missing social determinants data to emergency department (ED) revisits, and subsequent readmissions. Methods A deidentified data set of 123,697 people (18+ years), with at least one ED visit in 2017 at the University of Alabama at Birmingham Medical Center was used. The dependent variable was all-cause 30-day revisits (yes/no), while the independent variables were missing/nonmissing status of the social determinants of health measures. Logistic regression was used to test the relationship between likelihood of revisits and social determinants of health variables. Moreover, relative weight analysis was used to identify relative importance of the independent variables. Results Twelve social determinants were found to be most often missing. Of those 12, only “lives with” (alone or with family/friends) had higher odds of ED revisits. However, relative logistic weight analysis suggested that “pain score” and “activities of daily living” (ADL) accounted for almost 50% of the relevance for ED revisits when compared among all 12 variables. Conclusion In the process of care transition planning, data that are documented are factored into the care transition plan. One of the most common challenges in health services practice is to understand the value of missing data in effective program planning. This study suggests that the data that are not documented (i.e., missing) could play an important role in care transition planning as a mechanism to reduce ED revisits and eventual readmission rates.
      Citation: Appl Clin Inform 2020; 11: 556-563
      PubDate: 2020-08-26T00:00:00+01:00
      DOI: 10.1055/s-0040-1715650
      Issue No: Vol. 11, No. 04 (2020)
       
  • Development and Evaluation of a Fully Automated Surveillance System for
           Influenza-Associated Hospitalization at a Multihospital Health System in
           Northeast Ohio
    • Authors: Burke; Patrick C., Shirley, Rachel Benish, Raciniewski, Jacob, Simon, James F., Wyllie, Robert, Fraser, Thomas G.
      Pages: 564 - 569
      Abstract: Background Performing high-quality surveillance for influenza-associated hospitalization (IAH) is challenging, time-consuming, and essential. Objectives Our objectives were to develop a fully automated surveillance system for laboratory-confirmed IAH at our multihospital health system, to evaluate the performance of the automated system during the 2018 to 2019 influenza season at eight hospitals by comparing its sensitivity and positive predictive value to that of manual surveillance, and to estimate the time and cost savings associated with reliance on the automated surveillance system. Methods Infection preventionists (IPs) perform manual surveillance for IAH by reviewing laboratory records and making a determination about each result. For automated surveillance, we programmed a query against our Enterprise Data Vault (EDV) for cases of IAH. The EDV query was established as a dynamic data source to feed our data visualization software, automatically updating every 24 hours.To establish a gold standard of cases of IAH against which to evaluate the performance of manual and automated surveillance systems, we generated a master list of possible IAH by querying four independent information systems. We reviewed medical records and adjudicated whether each possible case represented a true case of IAH. Results We found 844 true cases of IAH, 577 (68.4%) of which were detected by the manual system and 774 (91.7%) of which were detected by the automated system. The positive predictive values of the manual and automated systems were 89.3 and 88.3%, respectively.Relying on the automated surveillance system for IAH resulted in an average recoup of 82 minutes per day for each IP and an estimated system-wide payroll redirection of $32,880 over the four heaviest weeks of influenza activity. Conclusion Surveillance for IAH can be entirely automated at multihospital health systems, saving time, and money while improving case detection.
      Citation: Appl Clin Inform 2020; 11: 564-569
      PubDate: 2020-08-26T00:00:00+01:00
      DOI: 10.1055/s-0040-1715651
      Issue No: Vol. 11, No. 04 (2020)
       
  • Implementation of Artificial Intelligence-Based Clinical Decision Support
           to Reduce Hospital Readmissions at a Regional Hospital
    • Authors: Romero-Brufau; Santiago, Wyatt, Kirk D., Boyum, Patricia, Mickelson, Mindy, Moore, Matthew, Cognetta-Rieke, Cheristi
      Pages: 570 - 577
      Abstract: Background Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. Objective The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. Methods A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. Results Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p 
      Citation: Appl Clin Inform 2020; 11: 570-577
      PubDate: 2020-09-02T00:00:00+01:00
      DOI: 10.1055/s-0040-1715827
      Issue No: Vol. 11, No. 04 (2020)
       
  • An Alternate Viewpoint on Information Sharing: There is no Paradox
    • Appl Clin Inform 2020; 11: 578-579
      DOI: 10.1055/s-0040-1715652



      Georg Thieme Verlag KG Stuttgart · New York

      Artikel in Thieme eJournals:
      Inhaltsverzeichnis     Volltext

      Appl Clin Inform 2020; 11: 578-5792020-09-02T00:00:00+01:00
      Issue No: Vol. 11, No. 04 (2020)
       
  • The Effect of Electronic Health Record Usability Redesign on Annual
           Screening Rates in an Ambulatory Setting
    • Authors: Pierce; Robert P., Eskridge, Bernie R., Rehard, LeAnn, Ross, Brandi, Day, Margaret A., Belden, Jeffery L.
      Pages: 580 - 588
      Abstract: Objectives Improving the usability of electronic health records (EHR) continues to be a focus of clinicians, vendors, researchers, and regulatory bodies. To understand the impact of usability redesign of an existing, site-configurable feature, we evaluated the user interface (UI) used to screen for depression, alcohol and drug misuse, fall risk, and the existence of advance directive information in ambulatory settings. Methods As part of a quality improvement project, based on heuristic analysis, the existing UI was redesigned. Using an iterative, user-centered design process, several usability defects were corrected. Summative usability testing was performed as part of the product development and implementation cycle. Clinical quality measures reflecting rolling 12-month rates of screening were examined over 8 months prior to the implementation of the redesigned UI and 9 months after implementation. Results Summative usability testing demonstrated improvements in task time, error rates, and System Usability Scale scores. Interrupted time series analysis demonstrated significant improvements in all screening rates after implementation of the redesigned UI compared with the original implementation. Conclusion User-centered redesign of an existing site-specific UI may lead to significant improvements in measures of usability and quality of patient care.
      Citation: Appl Clin Inform 2020; 11: 580-588
      PubDate: 2020-09-09T00:00:00+01:00
      DOI: 10.1055/s-0040-1715828
      Issue No: Vol. 11, No. 04 (2020)
       
  • Considerations for Designing EHR-Embedded Clinical Decision Support
           Systems for Antimicrobial Stewardship in Pediatric Emergency Departments
    • Authors: Ozkaynak; Mustafa, Metcalf, Noel, Cohen, Daniel M., May, Larissa S., Dayan, Peter S., Mistry, Rakesh D.
      Pages: 589 - 597
      Abstract: Objective This study was aimed to explore the intersection between organizational environment, workflow, and technology in pediatric emergency departments (EDs) and how these factors impact antibiotic prescribing decisions. Methods Semistructured interviews with 17 providers (1 fellow and 16 attending faculty), and observations of 21 providers (1 physician assistant, 5 residents, 3 fellows, and 12 attendings) were conducted at three EDs in the United States. We analyzed interview transcripts and observation notes using thematic analysis. Results Seven themes relating to antibiotic prescribing decisions emerged as follows: (1) professional judgement, (2) cognition as a critical individual resource, (3) decision support as a critical organizational resource, (4) patient management with imperfect information, (5) information-seeking as a primary task, (6) time management, and (7) broad process boundaries of antibiotic prescribing. Discussion The emerging interrelated themes identified in this study can be used as a blueprint to design, implement, and evaluate clinical decision support (CDS) systems that support antibiotic prescribing in EDs. The process boundaries of antibiotic prescribing are broader than the current boundaries covered by existing CDS systems. Incongruities between process boundaries and CDS can under-support clinicians and lead to suboptimal decisions. We identified two incongruities: (1) the lack of acknowledgment that the process boundaries go beyond the physical boundaries of the ED and (2) the lack of integration of information sources (e.g., accessibility to prior cultures on an individual patient outside of the organization). Conclusion Significant opportunities exist to improve appropriateness of antibiotic prescribing by considering process boundaries in the design, implementation, and evaluation of CDS systems.
      Citation: Appl Clin Inform 2020; 11: 589-597
      PubDate: 2020-09-09T00:00:00+01:00
      DOI: 10.1055/s-0040-1715893
      Issue No: Vol. 11, No. 04 (2020)
       
  • Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study
           of Underutilized Operational Data Streams in the Hospital Workplace
    • Authors: Womack; Dana M., Hribar, Michelle R., Steege, Linsey M., Vuckovic, Nancy H., Eldredge, Deborah H., Gorman, Paul N.
      Pages: 598 - 605
      Abstract: Background Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace activity, but little is known about the ability of these data to predict RN strain. Objectives The purpose of this study was to assess the utility of ambient workplace data, defined as time-stamped transaction records and log file data produced by non-electronic health record care delivery tools (e.g., nurse call systems, communication devices), as an information channel for automated sensing of RN strain. Methods In this exploratory retrospective study, ambient data for a 1-year time period were exported from electronic nurse call, medication dispensing, time and attendance, and staff communication systems. Feature sets were derived from these data for supervised machine learning models that classified work shifts by unplanned overtime. Models for three timeframes —8, 10, and 12 hours—were created to assess each model's ability to predict unplanned overtime at various points across the work shift. Results Classification accuracy ranged from 57 to 64% across three analysis timeframes. Accuracy was lowest at 10 hours and highest at shift end. Features with the highest importance include minutes spent using a communication device and percent of medications delivered via a syringe. Conclusion Ambient data streams can serve as information channels that contain signals related to unplanned overtime as a proxy indicator of RN strain as early as 8 hours into a work shift. This study represents an initial step toward enhanced detection of RN strain and proactive prevention of missed or delayed patient care.
      Citation: Appl Clin Inform 2020; 11: 598-605
      PubDate: 2020-09-16T00:00:00+01:00
      DOI: 10.1055/s-0040-1715829
      Issue No: Vol. 11, No. 04 (2020)
       
  • A Web Application for Adrenal Incidentaloma Identification, Tracking, and
           Management Using Machine Learning
    • Authors: Bala; Wasif, Steinkamp, Jackson, Feeney, Timothy, Gupta, Avneesh, Sharma, Abhinav, Kantrowitz, Jake, Cordella, Nicholas, Moses, James, Drake, Frederick Thurston
      Pages: 606 - 616
      Abstract: Background Incidental radiographic findings, such as adrenal nodules, are commonly identified in imaging studies and documented in radiology reports. However, patients with such findings frequently do not receive appropriate follow-up, partially due to the lack of tools for the management of such findings and the time required to maintain up-to-date lists. Natural language processing (NLP) is capable of extracting information from free-text clinical documents and could provide the basis for software solutions that do not require changes to clinical workflows. Objectives In this manuscript we present (1) a machine learning algorithm we trained to identify radiology reports documenting the presence of a newly discovered adrenal incidentaloma, and (2) the web application and results database we developed to manage these clinical findings. Methods We manually annotated a training corpus of 4,090 radiology reports from across our institution with a binary label indicating whether or not a report contains a newly discovered adrenal incidentaloma. We trained a convolutional neural network to perform this text classification task. Over the NLP backbone we built a web application that allows users to coordinate clinical management of adrenal incidentalomas in real time. Results The annotated dataset included 404 positive (9.9%) and 3,686 (90.1%) negative reports. Our model achieved a sensitivity of 92.9% (95% confidence interval: 80.9–97.5%), a positive predictive value of 83.0% (69.9–91.1)%, a specificity of 97.8% (95.8–98.9)%, and an F1 score of 87.6%. We developed a front-end web application based on the model's output. Conclusion Developing an NLP-enabled custom web application for tracking and management of high-risk adrenal incidentalomas is feasible in a resource constrained, safety net hospital. Such applications can be used by an institution's quality department or its primary care providers and can easily be generalized to other types of clinical findings.
      Citation: Appl Clin Inform 2020; 11: 606-616
      PubDate: 2020-09-16T00:00:00+01:00
      DOI: 10.1055/s-0040-1715892
      Issue No: Vol. 11, No. 04 (2020)
       
  • Empowering Caseworkers to Better Serve the Most Vulnerable with a
           Cloud-Based Care Management Solution
    • Authors: Snowdon; Jane L., Robinson, Barbie, Staats, Carolyn, Wolsey, Kenneth, Sands-Lincoln, Megan, Strasheim, Thomas, Brotman, David, Keating, Katie, Schnitter, Elizabeth, Jackson, Gretchen, Kassler, William
      Pages: 617 - 621
      Abstract: Background Care-management tools are typically utilized for chronic disease management. Sonoma County government agencies employed advanced health information technologies, artificial intelligence (AI), and interagency process improvements to help transform health and health care for socially disadvantaged groups and other displaced individuals. Objectives The objective of this case report is to describe how an integrated data hub and care-management solution streamlined care coordination of government services during a time of community-wide crisis. Methods This innovative application of care-management tools created a bridge between social and clinical determinants of health and used a three-step approach—access, collaboration, and innovation. The program Accessing Coordinated Care to Empower Self Sufficiency Sonoma was established to identify and match the most vulnerable residents with services to improve their well-being. Sonoma County created an Interdepartmental Multidisciplinary Team to deploy coordinated cross-departmental services (e.g., health and human services, housing services, probation) to support individuals experiencing housing insecurity. Implementation of a data integration hub (DIH) and care management and coordination system (CMCS) enabled integration of siloed data and services into a unified view of citizen status, identification of clinical and social determinants of health from structured and unstructured sources, and algorithms to match clients across systems. Results The integrated toolset helped 77 at-risk individuals in crisis through coordinated care plans and access to services in a time of need. Two case examples illustrate the specific care and services provided individuals with complex needs after the 2017 Sonoma County wildfires. Conclusion Unique application of a care-management solution transformed health and health care for individuals fleeing from their homes and socially disadvantaged groups displaced by the Sonoma County wildfires. Future directions include expanding the DIH and CMCS to neighboring counties to coordinate care regionally. Such solutions might enable innovative care-management solutions across a variety of public, private, and nonprofit services.
      Citation: Appl Clin Inform 2020; 11: 617-621
      PubDate: 2020-09-23T00:00:00+01:00
      DOI: 10.1055/s-0040-1715894
      Issue No: Vol. 11, No. 04 (2020)
       
  • A Rule-Based Data Quality Assessment System for Electronic Health Record
           Data
    • Authors: Wang; Zhan, Talburt, John R., Wu, Ningning, Dagtas, Serhan, Zozus, Meredith Nahm
      Pages: 622 - 634
      Abstract: Objective Rule-based data quality assessment in health care facilities was explored through compilation, implementation, and evaluation of 63,397 data quality rules in a single-center case study to assess the ability of rules-based data quality assessment to identify data errors of importance to physicians and system owners. Methods We applied a design science framework to design, demonstrate, test, and evaluate a scalable framework with which data quality rules can be managed and used in health care facilities for data quality assessment and monitoring. Results We identified 63,397 rules partitioned into 28 logic templates. A total of 819,683 discrepancies were identified by 4.5% of the rules. Nine out of 11 participating clinical and operational leaders indicated that the rules identified data quality problems and articulated next steps that they wanted to take based on the reported information. Discussion The combined rule template and knowledge table approach makes governance and maintenance of otherwise large rule sets manageable. Identified challenges to rule-based data quality monitoring included the lack of curated and maintained knowledge sources relevant to data error detection and lack of organizational resources to support clinical and operational leaders with investigation and characterization of data errors and pursuit of corrective and preventative actions. Limitations of our study included implementation within a single center and dependence of the results on the implemented rule set. Conclusion This study demonstrates a scalable framework (up to 63,397 rules) with which data quality rules can be implemented and managed in health care facilities to identify data errors. The data quality problems identified at the implementation site were important enough to prompt action requests from clinical and operational leaders.
      Citation: Appl Clin Inform 2020; 11: 622-634
      PubDate: 2020-09-23T00:00:00+01:00
      DOI: 10.1055/s-0040-1715567
      Issue No: Vol. 11, No. 04 (2020)
       
  • Clinical Decision Support for Worker Health: A Five-Site Qualitative Needs
           Assessment in Primary Care Settings
    • Authors: Ash; Joan S., Chase, Dian, Baron, Sherry, Filios, Margaret S., Shiffman, Richard N., Marovich, Stacey, Wiesen, Jane, Luensman, Genevieve B.
      Pages: 635 - 643
      Abstract: Background Although patients who work and have related health issues are usually first seen in primary care, providers in these settings do not routinely ask questions about work. Guidelines to help manage such patients are rarely used in primary care. Electronic health record (EHR) systems with worker health clinical decision support (CDS) tools have potential for assisting these practices. Objective This study aimed to identify the need for, and barriers and facilitators related to, implementation of CDS tools for the clinical management of working patients in a variety of primary care settings. Methods We used a qualitative design that included analysis of interview transcripts and observational field notes from 10 clinics in five organizations. Results We interviewed 83 providers, staff members, managers, informatics and information technology experts, and leaders and spent 35 hours observing. We identified eight themes in four categories related to CDS for worker health (operational issues, usefulness of proposed CDS, effort and time-related issues, and topic-specific issues). These categories were classified as facilitators or barriers to the use of the CDS tools. Facilitators related to operational issues include current technical feasibility and new work patterns associated with the coordinated care model. Facilitators concerning usefulness include users' need for awareness and evidence-based tools, appropriateness of the proposed CDS for their patients, and the benefits of population health data. Barriers that are effort-related include additional time this proposed CDS might take, and other pressing organizational priorities. Barriers that are topic-specific include sensitive issues related to health and work and the complexities of information about work. Conclusion We discovered several themes not previously described that can guide future CDS development: technical feasibility of the proposed CDS within commercial EHRs, the sensitive nature of some CDS content, and the need to assist the entire health care team in managing worker health.
      Citation: Appl Clin Inform 2020; 11: 635-643
      PubDate: 2020-09-30T00:00:00+01:00
      DOI: 10.1055/s-0040-1715895
      Issue No: Vol. 11, No. 04 (2020)
       
  • Accuracy of the Preferred Language Field in the Electronic Health Records
           of Two Canadian Hospitals
    • Authors: Rajaram; Akshay, Thomas, Daniel, Sallam, Faten, Verma, Amol A., Rawal, Shail
      Pages: 644 - 649
      Abstract: Background The collection of race, ethnicity, and language (REaL) data from patients is advocated as a first step to identify, monitor, and improve health inequities. As a result, many health care institutions collect patients' preferred languages in their electronic health records (EHRs). These data may be used in clinical care, research, and quality improvement. However, the accuracy of EHR language data are rarely assessed. Objectives This study aimed to audit the accuracy of EHR language data at two academic hospitals in Toronto, Ontario, Canada. Methods The EHR language was compared with a patient's stated preferred language by interview. Language was dichotomized to English or non-English. Agreement between language documented in the EHR and patient-reported preferred language was calculated using sensitivity, specificity, and positive predictive value (PPV). Results A total of 323 patients were interviewed, including 96 with a stated non-English preferred language. The sensitivity of the EHR for English-language preference was high at both hospitals: 100% at hospital A with a PPV of 88%, and 99% at hospital B with a PPV of 85%. However, the sensitivity of the EHR for non-English preference differed greatly between the two hospitals. The sensitivity was 81% with a PPV of 100% at hospital A and the sensitivity was 12% with a PPV of 60% at hospital B. Conclusion The accuracy of the EHR for identifying non-English language preference differed greatly between the hospitals studied. Language data must be accurate for it to be used, and regular quality assurance is required.
      Citation: Appl Clin Inform 2020; 11: 644-649
      PubDate: 2020-09-30T00:00:00+01:00
      DOI: 10.1055/s-0040-1715896
      Issue No: Vol. 11, No. 04 (2020)
       
  • Content Coverage Evaluation of the OMOP Vocabulary on the Transplant
           Domain Focusing on Concepts Relevant for Kidney Transplant Outcomes
           Analysis
    • Authors: Cho; Sylvia, Sin, Margaret, Tsapepas, Demetra, Dale, Leigh-Anne, Husain, Syed A., Mohan, Sumit, Natarajan, Karthik
      Pages: 650 - 658
      Abstract: Background Improving outcomes of transplant recipients within and across transplant centers is important with the increasing number of organ transplantations being performed. The current practice is to analyze the outcomes based on patient level data submitted to the United Network for Organ Sharing (UNOS). Augmenting the UNOS data with other sources such as the electronic health record will enrich the outcomes analysis, for which a common data model (CDM) can be a helpful tool for transforming heterogeneous source data into a uniform format. Objectives In this study, we evaluated the feasibility of representing concepts from the UNOS transplant registry forms with the Observational Medical Outcomes Partnership (OMOP) CDM vocabulary to understand the content coverage of OMOP vocabulary on transplant-specific concepts. Methods Two annotators manually mapped a total of 3,571 unique concepts extracted from the UNOS registry forms to concepts in the OMOP vocabulary. Concept mappings were evaluated by (1) examining the agreement among the initial two annotators and (2) investigating the number of UNOS concepts not mapped to a concept in the OMOP vocabulary and then classifying them. A subset of mappings was validated by clinicians. Results There was a substantial agreement between annotators with a kappa score of 0.71. We found that 55.5% of UNOS concepts could not be represented with OMOP standard concepts. The majority of unmapped UNOS concepts were categorized into transplant, measurement, condition, and procedure concepts. Conclusion We identified categories of unmapped concepts and found that some transplant-specific concepts do not exist in the OMOP vocabulary. We suggest that adding these missing concepts to OMOP would facilitate further research in the transplant domain.
      Citation: Appl Clin Inform 2020; 11: 650-658
      PubDate: 2020-10-07T00:00:00+01:00
      DOI: 10.1055/s-0040-1716528
      Issue No: Vol. 11, No. 04 (2020)
       
  • Direct Observational Study of Interfaced Smart-Pumps in Pediatric
           Intensive Care
    • Authors: Howlett; Moninne M., Breatnach, Cormac V., Brereton, Erika, Cleary, Brian J.
      Pages: 659 - 670
      Abstract: Background Processes for delivery of high-risk infusions in pediatric intensive care units (PICUs) are complex. Standard concentration infusions (SCIs), smart-pumps, and electronic prescribing are recommended medication error reduction strategies. Implementation rates in Europe lag behind those in the United States. Since 2012, the PICU of an Irish tertiary pediatric hospital has been using a smart-pump SCI library, interfaced with electronic infusion orders (Philips ICCA). The incidence of infusion errors is unknown. Objectives To determine the frequency, severity, and distribution of smart-pump infusion errors in PICUs. Methods Programmed infusions were directly observed at the bedside. Parameters were compared against medication orders and autodocumented infusion data. Identified deviations were categorized as medication errors or discrepancies. Error rates (%) were calculated as infusions with errors and errors per opportunities for error (OEs). Predefined definitions, multidisciplinary consensus and grading processes were employed. Results A total of 1,023 infusions for 175 patients were directly observed over 27 days between February and September 2017. The drug library accommodated 96.5% of infusions. Compliance with the drug library was 98.9%. A total of 133 infusions had ≥1 error (13.0%); a further 58 (5.7%) had ≥1 discrepancy. From a total of 4,997 OEs, 153 errors (3.1%) and 107 discrepancies (2.1%) were observed. Undocumented bolus doses were most commonly identified (n = 81); this was the only deviation in 36.1% (n = 69) of infusions. Programming errors were rare (0.32% OE). Errors were minor, with just one requiring minimal intervention to prevent harm. Conclusion The error rates identified are low compared with similar studies, highlighting the benefits of smart-pumps and autodocumented infusion data in PICUs. A range of quality improvement opportunities has been identified.
      Citation: Appl Clin Inform 2020; 11: 659-670
      PubDate: 2020-10-07T00:00:00+01:00
      DOI: 10.1055/s-0040-1716527
      Issue No: Vol. 11, No. 04 (2020)
       
  • Patient-Initiated Data: Our Experience with Enabling Patients to Initiate
           Incorporation of Heart Rate Data into the Electronic Health Record
    • Authors: Pevnick; Joshua M., Elad, Yaron, Masson, Lisa M., Riggs, Richard V., Duncan, Ray G.
      Pages: 671 - 679
      Abstract: Background Provider organizations increasingly allow incorporation of patient-generated data into electronic health records (EHRs). In 2015, we began allowing patients to upload data to our EHR without physician orders, which we henceforth call patient-initiated data (PAIDA). Syncing wearable heart rate monitors to our EHR allows for uploading of thousands of heart rates per patient per week, including many abnormally low and high rates. Physician informaticists expressed concern that physicians and their patients might be unaware of abnormal heart rates, including those caused by treatable pathology. Objective This study aimed to develop a protocol to address millions of unreviewed heart rates. Methods As a quality improvement initiative, we assembled a physician informaticist team to meet monthly for review of abnormally low and high heart rates. By incorporating other data already present in the EHR, lessons learned from reviewing records over time, and from contacting physicians, we iteratively refined our protocol. Results We developed (1) a heart rate visualization dashboard to identify concerning heart rates; (2) experience regarding which combinations of heart rates and EHR data were most clinically worrisome, as opposed to representing artifact; (3) a protocol whereby only concerning heart rates would trigger a cardiologist review revealing protected health information; and (4) a generalizable framework for addressing other PAIDA. Conclusion We expect most PAIDA to eventually require systematic integration and oversight. Our governance framework can help guide future efforts, especially for cases with large amounts of data and where abnormal values may represent concerning but treatable pathology.
      Citation: Appl Clin Inform 2020; 11: 671-679
      PubDate: 2020-10-14T00:00:00+01:00
      DOI: 10.1055/s-0040-1716538
      Issue No: Vol. 11, No. 04 (2020)
       
  • Graphical Presentations of Clinical Data in a Learning Electronic Medical
           Record
    • Authors: Calzoni; Luca, Clermont, Gilles, Cooper, Gregory F., Visweswaran, Shyam, Hochheiser, Harry
      Pages: 680 - 691
      Abstract: Background Complex electronic medical records (EMRs) presenting large amounts of data create risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes models of intensive care unit (ICU) physicians' data access patterns to identify and then highlight the most relevant data for each patient. Objectives We used insights from literature and feedback from potential users to inform the design of an EMR display capable of highlighting relevant information. Methods We used a review of relevant literature to guide the design of preliminary paper prototypes of the LEMR user interface. We observed five ICU physicians using their current EMR systems in preparation for morning rounds. Participants were interviewed and asked to explain their interactions and challenges with the EMR systems. Findings informed the revision of our prototypes. Finally, we conducted a focus group with five ICU physicians to elicit feedback on our designs and to generate ideas for our final prototypes using participatory design methods. Results Participating physicians expressed support for the LEMR system. Identified design requirements included the display of data essential for every patient together with diagnosis-specific data and new or significantly changed information. Respondents expressed preferences for fishbones to organize labs, mouseovers to access additional details, and unobtrusive alerts minimizing color-coding. To address the concern about possible physician overreliance on highlighting, participants suggested that non-highlighted data should remain accessible. Study findings led to revised prototypes, which will inform the development of a functional user interface. Conclusion In the feedback we received, physicians supported pursuing the concept of a LEMR system. By introducing novel ways to support physicians' cognitive abilities, such a system has the potential to enhance physician EMR use and lead to better patient outcomes. Future plans include laboratory studies of both the utility of the proposed designs on decision-making, and the possible impact of any automation bias.
      Citation: Appl Clin Inform 2020; 11: 680-691
      PubDate: 2020-10-14T00:00:00+01:00
      DOI: 10.1055/s-0040-1709707
      Issue No: Vol. 11, No. 04 (2020)
       
 
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